A modern data infrastructure is essential for retailers looking to stay competitive today. Companies are abandoning more traditional, on-premises IT infrastructures and moving to more centralized “as a service” (XaaS) models of delivery enabled by cloud technologies, according to McKinsey. Aging on-premises infrastructures are unable to meet demands for agility and innovation, eating up too much time and too many resources for teams trying to maintain them.

We’re entering an age where many organizations are trying to increase productivity by transforming themselves into digital organizations. Digital organizations are built on top of digital architectures that unlock agility across the enterprise. Agility, which is the ability to respond to changes and new needs fast, enables most of the other advantages such as shorter times to market, better customer experiences, and competitive advantages in the long run.

Financial inclusion, defined as the availability and accessibility of financial services to underserved communities, is a critical issue facing the banking industry today. According to the World Bank, 1.7 billion adults around the world do not have access to formal financial services, meaning that they cannot open a bank account or access credit, insurance, or other financial products.

Software testing has become increasingly complex thanks to the twin threat of growing complexity and higher user expectations. Fortunately, testing tools continue to evolve, becoming easier to use, more feature-full, and offering better test coverage. Cypress is a prime example of these improvements in end-to-end testing. Let's look at what end-to-end testing involves and how you can use Cypress and BitBar to streamline the process.

“Good software, like wine, takes time” – Avram Joel Spolsky But do we have that much time? The statement was perfect when SDLC still relied on the waterfall and agile methodologies. But the scenario has drastically changed since then. DevOps is ruling the SDLC world today, and tools supporting the process are everyone’s favorite. And CI/CD is the modern software development practice every DevOps engineer swears by.

Kubernetes is a powerful platform that comes with many features to help engineers run their applications more efficiently. However, as you gain more experience and deploy more workloads, you’ll inevitably start looking for ways to optimize Kubernetes performance. There are many ways to approach optimization. On one hand, you could work exclusively with the tools and configurations provided by Kubernetes itself; on the other, you could reap the benefits of third-party tools.

While ‘Smoke Testing’, ‘Sanity Testing’, and ‘Regression Testing’ are widely practiced in the world of QA, there still exist some misinterpretations or misconceptions around these concepts. For your team to make the most optimal use of the 3 methods, this article will help you to understand and differentiate them from one another.

More than 70% of automation script failures are false and are caused by poor automation design practices. The False Failure Rate (FFR) is the percentage of tests that falsely fail when a test suite is run. It is calculated by dividing the number of false failures (failed tests that actually passed) by the total number of tests that were executed during a test run. Higher false failure rates increase testing and maintenance costs.

Ever since computers started to make their way into the workplace, application development has been a priority for businesses. As a company grows, there is a need for new applications to handle new business processes and make the most of new opportunities. However, the list of custom applications requested can turn into a backlog as you hit the limits of developer resources. To ease this burden, the concept of low-code application development has entered the enterprise.

As the field of software development continues to evolve, one cannot help but wonder about the future of the profession. With the integration of AI tools, like ChatGPT, and Machine Learning, tasks that were once exclusive to human developers are now being handled by machines. While this has the potential to greatly increase efficiency and productivity, it also raises important questions about the future of the developer profession. How will developers adapt to this new reality?